Monthly Archives: May 2014

Self-Reconfigurable Smart Camera Networks

Self-Reconfigurable Smart Camera Networks

  • Juan C. SanMiguel, Christian Micheloni, Karen Shoop, Gian Luca Foresti, and Andrea Cavallaro. Self-Reconfigurable Smart Camera Networks. IEEE Computer, 47(5):67-73, 2014. doi:10.1109/MC.2014.133
    [BibTeX] [Abstract]

    Camera networks that reconfigure while performing multiple tasks have unique requirements, such as concurrent task allocation with limited resources, the sharing of data among fields of view across the network, and coordination among heterogeneous devices.

    @Article{2014-05-SANMIGUEL,
    title={{Self-Reconfigurable Smart Camera Networks}},
    author={Juan C. SanMiguel and Christian Micheloni and Karen Shoop and Gian Luca Foresti and Andrea Cavallaro},
    journal={{IEEE Computer}},
    volume={47},
    number={5},
    pages={67-73},
    date={2014-05-20},
    doi={10.1109/MC.2014.133},
    year={2014},
    abstract={Camera networks that reconfigure while performing multiple tasks have unique requirements, such as concurrent task allocation with limited resources, the sharing of data among fields of view across the network, and coordination among heterogeneous devices.}
    }

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Sposob lokalizacji osob i/lub obiektow wewnatrz i/lub na zewnatrz budynkow droga komunikacji optycznej i radiowej, zwla-sz-cza w warunkach zaklocen optycznych oraz uklad do realizacji tego sposobu

Sposob lokalizacji osob i/lub obiektow wewnatrz i/lub na zewnatrz budynkow droga komunikacji optycznej i radiowej, zwla-sz-cza w warunkach zaklocen optycznych oraz uklad do realizacji tego sposobu

  • Krzysztof Nyka, Lukasz Kulas, and Przemyslaw Woźnica. Sposób lokalizacji osób ilub obiektów wewnatrz ilub na zewnatrz budynków droga komunikacji optycznej i radiowej, zwłaszcza w warunkach wystepowania zakłóceń optycznych oraz układ do realizacji sposobu. Patent (P.408187), Gdansk University of Technology, Poland, 2014.
    [BibTeX]
    @Misc{2014-05-NYKA,
    author = {Krzysztof Nyka and Lukasz Kulas and Przemyslaw Wo\'{z}nica},
    title = {{Spos\'{o}b lokalizacji os\'{o}b ilub obiekt\'{o}w wewnatrz ilub na zewnatrz budynk\'{o}w droga komunikacji optycznej i radiowej, zw\l{}aszcza w warunkach wystepowania zak\l{}\'{o}ce\'{n} optycznych oraz uk\l{}ad do realizacji sposobu}},
    date = {2014-05-12},
    year = {2014},
    howpublished = {Patent (P.408187), Gdansk University of Technology, Poland}
    }

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Parallel particle-PHD filter

Parallel particle-PHD filter

  • Marco Del Coco and Andrea Cavallaro. Parallel particle-PHD filter. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Florence, Italy, 2014. doi:10.1109/ICASSP.2014.6854872
    [BibTeX] [Abstract]

    The complexity of multi-target tracking grows faster than linearly with the increase of the numbers of objects, thus making the design of real-time trackers a challenging task for scenarios with a large number of targets. The Probability Hypothesis Density (PHD) filter is known to help reducing this complexity. However, this reduction may not suffice in critical situations when the number of targets, dimension of the state vector, clutter conditions and sample rate are high. To address this problem, we propose a parallelization scheme for the particle PHD filter. The proposed scheme exploits the knowledge of mutual interacting targets in the scene to help fragmentation and to reduce the workload of individual processors. We compare the proposed approach with alternative parallelization schemes and discuss its advantages and limitations using the results obtained on two multi-target tracking datasets.

    @InProceedings{2014-05-COCO,
    title = {{Parallel particle-PHD filter}},
    author = {Marco Del Coco and Andrea Cavallaro},
    booktitle = {{Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing}},
    address= {Florence, Italy},
    date = {2014-05-04/2014-05-09},
    year = {2014},
    doi = {10.1109/ICASSP.2014.6854872},
    abstract = {The complexity of multi-target tracking grows faster than linearly with the increase of the numbers of objects, thus making the design of real-time trackers a challenging task for scenarios with a large number of targets. The Probability Hypothesis Density (PHD) filter is known to help reducing this complexity. However, this reduction may not suffice in critical situations when the number of targets, dimension of the state vector, clutter conditions and sample rate are high. To address this problem, we propose a parallelization scheme for the particle PHD filter. The proposed scheme exploits the knowledge of mutual interacting targets in the scene to help fragmentation and to reduce the workload of individual processors. We compare the proposed approach with alternative parallelization schemes and discuss its advantages and limitations using the results obtained on two multi-target tracking datasets.}
    }

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Predicting and recognizing Interactions in Public Spaces

Predicting and recognizing Interactions in Public Spaces

  • Fabio Poiesi and Andrea Cavallaro. Predicting and recognizing Interactions in Public Spaces. Journal of Real-Time Image Processing, 10(4):785-803, 2014. doi:10.1007/s11554-014-0428-8
    [BibTeX] [Abstract]

    We present an extensive survey of methods for recognizing human interactions and propose a method for predicting rendezvous areas in observable and unobservable regions using sparse motion information. Rendezvous areas indicate where people are likely to interact with each other or with static objects (e.g., a door, an information desk or a meeting point). The proposed method infers the direction of movement by calculating prediction lines from displacement vectors and temporally accumulates intersecting locations generated by prediction lines. The intersections are then used as candidate rendezvous areas and modeled as spatial probability density functions using Gaussian Mixture Models. We validate the proposed method to predict dynamic and static rendezvous areas on real-world datasets and compare it with related approaches.

    @Article{2014-05-POIESI,
    author = {Fabio Poiesi and Andrea Cavallaro},
    title = {{Predicting and recognizing Interactions in Public Spaces}},
    journal = {{Journal of Real-Time Image Processing}},
    volume = {10},
    number = {4},
    pages = {785-803},
    date = {2014-05},
    doi = {10.1007/s11554-014-0428-8},
    abstract = {We present an extensive survey of methods for recognizing human interactions and propose a method for predicting rendezvous areas in observable and unobservable regions using sparse motion information. Rendezvous areas indicate where people are likely to interact with each other or with static objects (e.g., a door, an information desk or a meeting point). The proposed method infers the direction of movement by calculating prediction lines from displacement vectors and temporally accumulates intersecting locations generated by prediction lines. The intersections are then used as candidate rendezvous areas and modeled as spatial probability density functions using Gaussian Mixture Models. We validate the proposed method to predict dynamic and static rendezvous areas on real-world datasets and compare it with related approaches.},
    year = {2014}
    }

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